




"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)  # pylint: disable=invalid-name

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Dict:
       super().__init__()

       if safety_checker is None:
           logger.warning(
               F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
               ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
               ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
               ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
               ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
               ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''')

       self.register_modules(
           speech_model=lowerCamelCase_       ,					speech_processor=lowerCamelCase_       ,					vae=lowerCamelCase_       ,					text_encoder=lowerCamelCase_       ,					tokenizer=lowerCamelCase_       ,					unet=lowerCamelCase_       ,					scheduler=lowerCamelCase_       ,					feature_extractor=lowerCamelCase_       ,					)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_ = "auto")				->   str:
       if slice_size == "auto":
           UpperCamelCase              =	self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       self.enable_attention_slicing(lowerCamelCase_)







   @torch.no_grad()
   def __call__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=1_6_0_0_0       ,					lowerCamelCase_ = 5_1_2       ,					lowerCamelCase_ = 5_1_2       ,					lowerCamelCase_ = 5_0       ,					lowerCamelCase_ = 7.5       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 1       ,					lowerCamelCase_ = 0.0       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = "pil"       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = 1       ,					**lowerCamelCase_       ,					)				->   Any:
       UpperCamelCase              =	self.speech_processor.feature_extractor(
           lowerCamelCase_       ,					return_tensors='''pt'''       ,					sampling_rate=lowerCamelCase_).input_features.to(self.device)
       UpperCamelCase              =	self.speech_model.generate(lowerCamelCase_       ,					max_length=4_8_0_0_0_0)

       UpperCamelCase              =	self.speech_processor.tokenizer.batch_decode(lowerCamelCase_       ,					skip_special_tokens=lowerCamelCase_       ,					normalize=lowerCamelCase_)[
           0
       ]

       if isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	1
       elif isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	len(lowerCamelCase_)
       else:
           raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_)}')

       if height % 8 != 0 or width % 8 != 0:
           raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(lowerCamelCase_       ,					lowerCamelCase_) or callback_steps <= 0)
       ):
           raise ValueError(
               F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
               F' {type(lowerCamelCase_)}.')

       # get prompt text embeddings
       UpperCamelCase              =	self.tokenizer(
           lowerCamelCase_       ,					padding='''max_length'''       ,					max_length=self.tokenizer.model_max_length       ,					return_tensors='''pt'''       ,					)
       UpperCamelCase              =	text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           UpperCamelCase              =	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
           logger.warning(
               '''The following part of your input was truncated because CLIP can only handle sequences up to'''
               F' {self.tokenizer.model_max_length} tokens: {removed_text}')
           UpperCamelCase              =	text_input_ids[:, : self.tokenizer.model_max_length]
       UpperCamelCase              =	self.text_encoder(text_input_ids.to(self.device))[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	text_embeddings.shape
       UpperCamelCase              =	text_embeddings.repeat(1       ,					lowerCamelCase_       ,					1)
       UpperCamelCase              =	text_embeddings.view(bs_embed * num_images_per_prompt       ,					lowerCamelCase_       ,					-1)

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       UpperCamelCase              =	guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
           UpperCamelCase              =	42
           if negative_prompt is None:
               UpperCamelCase              =	[''''''] * batch_size
           elif type(lowerCamelCase_) is not type(lowerCamelCase_):
               raise TypeError(
                   F'`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_)} !='
                   F' {type(lowerCamelCase_)}.')
           elif isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               UpperCamelCase              =	[negative_prompt]
           elif batch_size != len(lowerCamelCase_):
               raise ValueError(
                   F'`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_)}, but `prompt`:'
                   F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                   ''' the batch size of `prompt`.''')
           else:
               UpperCamelCase              =	negative_prompt

           UpperCamelCase              =	text_input_ids.shape[-1]
           UpperCamelCase              =	self.tokenizer(
               lowerCamelCase_       ,					padding='''max_length'''       ,					max_length=lowerCamelCase_       ,					truncation=lowerCamelCase_       ,					return_tensors='''pt'''       ,					)
           UpperCamelCase              =	self.text_encoder(uncond_input.input_ids.to(self.device))[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase              =	uncond_embeddings.shape[1]
           UpperCamelCase              =	uncond_embeddings.repeat(1       ,					lowerCamelCase_       ,					1)
           UpperCamelCase              =	uncond_embeddings.view(batch_size * num_images_per_prompt       ,					lowerCamelCase_       ,					-1)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           UpperCamelCase              =	torch.cat([uncond_embeddings, text_embeddings])

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       UpperCamelCase              =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase              =	text_embeddings.dtype
       if latents is None:
           if self.device.type == "mps":
               # randn does not exist on mps
               UpperCamelCase              =	torch.randn(lowerCamelCase_       ,					generator=lowerCamelCase_       ,					device='''cpu'''       ,					dtype=lowerCamelCase_).to(
                   self.device)
           else:
               UpperCamelCase              =	torch.randn(lowerCamelCase_       ,					generator=lowerCamelCase_       ,					device=self.device       ,					dtype=lowerCamelCase_)
       else:
           if latents.shape != latents_shape:
               raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
           UpperCamelCase              =	latents.to(self.device)

       # set timesteps
       self.scheduler.set_timesteps(lowerCamelCase_)

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCamelCase              =	self.scheduler.timesteps.to(self.device)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCamelCase              =	latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCamelCase              =	'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
       UpperCamelCase              =	{}
       if accepts_eta:
           UpperCamelCase              =	eta

       for i, t in enumerate(self.progress_bar(lowerCamelCase_)):
           # expand the latents if we are doing classifier free guidance
           UpperCamelCase              =	torch.cat([latents] * 2) if do_classifier_free_guidance else latents
           UpperCamelCase              =	self.scheduler.scale_model_input(lowerCamelCase_       ,					lowerCamelCase_)

           # predict the noise residual
           UpperCamelCase              =	self.unet(lowerCamelCase_       ,					lowerCamelCase_       ,					encoder_hidden_states=lowerCamelCase_).sample

           # perform guidance
           if do_classifier_free_guidance:
               UpperCamelCase       ,	UpperCamelCase              =	noise_pred.chunk(2)
               UpperCamelCase              =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # compute the previous noisy sample x_t -> x_t-1
           UpperCamelCase              =	self.scheduler.step(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					**lowerCamelCase_).prev_sample

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
               callback(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	1 / 0.1_8215 * latents
       UpperCamelCase              =	self.vae.decode(lowerCamelCase_).sample

       UpperCamelCase              =	(image / 2 + 0.5).clamp(0       ,					1)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       UpperCamelCase              =	image.cpu().permute(0       ,					2       ,					3       ,					1).float().numpy()

       if output_type == "pil":
           UpperCamelCase              =	self.numpy_to_pil(lowerCamelCase_)

       if not return_dict:
           return image

       return StableDiffusionPipelineOutput(images=lowerCamelCase_       ,					nsfw_content_detected=lowerCamelCase_)
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Dict         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowercase					     =				[144, 192, 240]
								__lowercase					     =				[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowercase					     =				[96, 120, 144]
								__lowercase					     =				[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowercase					     =				[64, 80, 96]
								__lowercase					     =				[16, 16, 24, 48, 64, 80, 320]
								__lowercase					     =				0.05
								__lowercase					     =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				512
								__lowercase					     =				16
								__lowercase					     =				21
								__lowercase					     =				'''pascal-voc-id2label.json'''
				else:
								__lowercase					     =				1000
								__lowercase					     =				'''imagenet-1k-id2label.json'''

				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				for i in range(1				,     6				):
								if F"layer_{i}." in name:
												__lowercase					     =				name.replace(F"layer_{i}."				,     F"encoder.layer.{i - 1}."				)

				if "conv_1." in name:
								__lowercase					     =				name.replace('''conv_1.'''				,     '''conv_stem.'''				)
				if ".block." in name:
								__lowercase					     =				name.replace('''.block.'''				,     '''.'''				)

				if "exp_1x1" in name:
								__lowercase					     =				name.replace('''exp_1x1'''				,     '''expand_1x1'''				)
				if "red_1x1" in name:
								__lowercase					     =				name.replace('''red_1x1'''				,     '''reduce_1x1'''				)
				if ".local_rep.conv_3x3." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_3x3.'''				,     '''.conv_kxk.'''				)
				if ".local_rep.conv_1x1." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_1x1.'''				,     '''.conv_1x1.'''				)
				if ".norm." in name:
								__lowercase					     =				name.replace('''.norm.'''				,     '''.normalization.'''				)
				if ".conv." in name:
								__lowercase					     =				name.replace('''.conv.'''				,     '''.convolution.'''				)
				if ".conv_proj." in name:
								__lowercase					     =				name.replace('''.conv_proj.'''				,     '''.conv_projection.'''				)

				for i in range(0				,     2				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}.layer.{j}."				)

				for i in range(2				,     6				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}."				)
																if "expand_1x1" in name:
																				__lowercase					     =				name.replace('''expand_1x1'''				,     '''downsampling_layer.expand_1x1'''				)
																if "conv_3x3" in name:
																				__lowercase					     =				name.replace('''conv_3x3'''				,     '''downsampling_layer.conv_3x3'''				)
																if "reduce_1x1" in name:
																				__lowercase					     =				name.replace('''reduce_1x1'''				,     '''downsampling_layer.reduce_1x1'''				)

				for i in range(2				,     5				):
								if F".global_rep.{i}.weight" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.weight"				,     '''.layernorm.weight'''				)
								if F".global_rep.{i}.bias" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.bias"				,     '''.layernorm.bias'''				)

				if ".global_rep." in name:
								__lowercase					     =				name.replace('''.global_rep.'''				,     '''.transformer.'''				)
				if ".pre_norm_mha.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.0.'''				,     '''.layernorm_before.'''				)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.1.out_proj.'''				,     '''.attention.output.dense.'''				)
				if ".pre_norm_ffn.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.0.'''				,     '''.layernorm_after.'''				)
				if ".pre_norm_ffn.1." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.1.'''				,     '''.intermediate.dense.'''				)
				if ".pre_norm_ffn.4." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.4.'''				,     '''.output.dense.'''				)
				if ".transformer." in name:
								__lowercase					     =				name.replace('''.transformer.'''				,     '''.transformer.layer.'''				)

				if ".aspp_layer." in name:
								__lowercase					     =				name.replace('''.aspp_layer.'''				,     '''.'''				)
				if ".aspp_pool." in name:
								__lowercase					     =				name.replace('''.aspp_pool.'''				,     '''.'''				)
				if "seg_head." in name:
								__lowercase					     =				name.replace('''seg_head.'''				,     '''segmentation_head.'''				)
				if "segmentation_head.classifier.classifier." in name:
								__lowercase					     =				name.replace('''segmentation_head.classifier.classifier.'''				,     '''segmentation_head.classifier.'''				)

				if "classifier.fc." in name:
								__lowercase					     =				name.replace('''classifier.fc.'''				,     '''classifier.'''				)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowercase					     =				'''mobilevit.''' + name

				return name





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				if base_model:
								__lowercase					     =				''''''
				else:
								__lowercase					     =				'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowercase					     =				orig_state_dict.pop(lowercase				)

								if key[:8] == "encoder.":
												__lowercase					     =				key[8:]

								if "qkv" in key:
												__lowercase					     =				key.split('''.'''				)
												__lowercase					     =				int(key_split[0][6:]				) - 1
												__lowercase					     =				int(key_split[3]				)
												__lowercase					     =				model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"				)
												__lowercase					     =				layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__lowercase					     =				(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__lowercase					     =				val[:dim, :]
																__lowercase					     =				val[dim : dim * 2, :]
																__lowercase					     =				val[-dim:, :]
												else:
																__lowercase					     =				val[:dim]
																__lowercase					     =				val[dim : dim * 2]
																__lowercase					     =				val[-dim:]
								else:
												__lowercase					     =				val

				return orig_state_dict





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				get_mobilevit_config(lowercase				)

				# load original state_dict
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				MobileViTForSemanticSegmentation(lowercase				).eval()
				else:
								__lowercase					     =				MobileViTForImageClassification(lowercase				).eval()

				__lowercase					     =				convert_state_dict(lowercase				,     lowercase				)
				model.load_state_dict(lowercase				)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowercase					     =				MobileViTImageProcessor(crop_size=config.image_size				,     size=config.image_size + 32				)
				__lowercase					     =				image_processor(images=prepare_img()				,     return_tensors='''pt'''				)
				__lowercase					     =				model(**lowercase				)
				__lowercase					     =				outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''				):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowercase					     =				torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3, :3, :3]				,     lowercase				,     atol=1E-4				)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowercase					     =				torch.tensor([-0.9866, 0.2392, -1.1241]				)
								elif mobilevit_name == "mobilevit_xs":
												__lowercase					     =				torch.tensor([-2.4761, -0.9399, -1.9587]				)
								elif mobilevit_name == "mobilevit_xxs":
												__lowercase					     =				torch.tensor([-1.9364, -1.2327, -0.4653]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3]				,     lowercase				,     atol=1E-4				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)
				print(F"Saving image processor to {pytorch_dump_folder_path}"				)
				image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								__lowercase					     =				{
								    '''mobilevit_s''': '''mobilevit-small''',
								    '''mobilevit_xs''': '''mobilevit-x-small''',
								    '''mobilevit_xxs''': '''mobilevit-xx-small''',
								    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
								    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
								    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
								}

								print('''Pushing to the hub...'''				)
								__lowercase					     =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowercase				,     organization='''apple'''				)
								model.push_to_hub(lowercase				,     organization='''apple'''				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--mobilevit_name""",
		    default="""mobilevit_s""",
		    type=str,
		    help=(
		        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
		        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
		    ),
		)
		parser.add_argument(
		    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	Dict         =    parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)